Executive Director, Translational & Computational Biology Team

What You'll Do

• Lead High-Impact Translational and Computational Programs
• Identify and prioritize questions where integrated biological, clinical, and computational evidence can materially change a scientific or development decision
• Lead scientific strategy from problem formulation through analysis, validation, interpretation, and recommendation
• Generate and test hypotheses that connect molecular mechanisms to patient biology, treatment response, and development strategy
• Ensure work is tied to measurable scientific, development, or portfolio outcomes
• Translate Scientific Questions into AI and Analytical Strategies
• Partner with AI scientists, engineers, computational biologists, statisticians, and domain experts to define fit-for-purpose approaches
• Define cohorts, endpoints, labels, biological annotations, evaluation criteria, and validation plans that reflect biological and clinical reality
• Guide selection and application of computational methods and AI capabilities, including where new methods or models are required
• Challenge outputs for biological plausibility, robustness, generalizability, uncertainty, and decision relevance
• Partner Across Therapeutic Areas and Development
• Serve as a trusted advisor to scientific and clinical leaders across discovery, translational medicine, and clinical development
• Bring emerging AI capabilities into therapeutic-area workflows and help teams identify where they can create meaningful scientific advantage
• Build durable partnerships across R&D functions, connecting scientific needs, technical capabilities, and evidence
• Represent translational and computational perspectives in cross-functional program and portfolio discussions
• Integrate Diverse Scientific Data
• Lead or guide analyses involving multi-omics data, single-cell and spatial biology, genetics and genomics, perturbational and functional biology, imaging and digital pathology, clinical trial datasets, real-world data and evidence, scientific literature, internal knowledge, and other multimodal data sources
• Set Standards for Scientific and Analytical Judgment
• Interpret results in biological, clinical, and development context
• Assess uncertainty, confounding, bias, alternative explanations, and failure modes
• Establish fit-for-purpose standards for rigor, reproducibility, validation, and evidence
• Distinguish statistically interesting findings from evidence that is actionable for R&D
• Scale Impact Through Partnership and Adoption
• Communicate complex evidence clearly to scientific, clinical, technical, and senior leadership audiences
• Influence decisions in matrixed teams through scientific credibility, judgment, and execution
• Mentor and guide scientists and cross-functional colleagues; elevate translational and computational fluency across teams
• Serve as an early adopter of AI-enabled ways of working and help embed successful approaches into repeatable scientific practice

What You Bring

Education & Scientific Leadership: A PhD, MD, PharmD, or equivalent advanced degree in Computational Biology, Translational Biology, Bioinformatics, Genetics, Genomics, Systems Biology, Biomedical Sciences, Biostatistics, or a related discipline with 10+ years of relevant industry or research experience; or a Master's degree with 12+ years of relevant experience, or an equivalent record of scientific and translational impact. Most importantly, you have a sustained record of translating complex biological and clinical data into insights that influenced meaningful scientific, development, or portfolio decisions.

Demonstrated Strategic and Scientific Leadership: Experience leading cross-functional scientific or translational programs from an ambiguous question to an evidence-based decision; defining strategy and priorities for complex analytical, biomarker, precision-medicine, or data-driven work; applying computational and quantitative approaches to real-world biological and clinical questions; influencing senior stakeholders through clear scientific reasoning, evidence, and recommendations; operating with substantial autonomy in matrixed organizations and aligning contributors without relying on formal authority.

Computational & AI Fluency: Working or strong proficiency in R and/or Python, or equivalent computational tools sufficient to work directly with data and analytical workflows; statistical reasoning, experimental design, and quantitative interpretation; biological and clinical data analysis and visualization; reproducible research and evidence-generation practices; machine learning and AI concepts relevant to biomedical research; evaluating model outputs, uncertainty, limitations, and scientific validity. Demonstrated depth in one or more major scientific domains such as multi-omics, single-cell or spatial biology, genetics and genomics, translational or systems biology, imaging or digital pathology, clinical development and biomarker science, real-world data and evidence. Experience integrating multiple data types and working with AI-enabled scientific workflows is strongly valued.

Leadership, Judgment & Scientific Mindset: Deep intellectual curiosity and biological judgment; strategic thinking and the ability to identify the questions that matter most; scientific rigor, healthy skepticism, and evidence-based decision-making; comfort operating with ambiguity and high autonomy; ability to move fluidly between biology, data, AI, and drug-development context; executive-level communication and influence across scientific and technical audiences; a collaborative, low-ego approach to building trust and enabling others.

Preferred Qualifications

• Significant experience in biotechnology or pharmaceutical R&D
• Demonstrated impact across drug discovery, translational research, clinical development, precision medicine, or biomarker strategy
• Experience applying or adopting AI-enabled scientific workflows in real R&D settings
• Experience leading multidisciplinary teams or major cross-functional workstreams
• Deep expertise in one or more therapeutic areas and a track record of translating computational evidence into program or portfolio decisions